The present invention relates to wireless networks, and more specifically, to quality of information (QoI) assessment in dynamic sensor networks.
With the increasing reliance on more and more sensors to monitor the state of objects and processes of interest (e.g., humans, engines, structures, environment, traffic, utilities, water supplies, etc.) for various smart planet applications, the QoI reported by sensors becomes more important to sensor aided decision making processes. QoI relates to the level of trust one can place in the validity of a piece of information to determine, for example, whether an event has really occurred, whether the event occurred at the time and place noted in a report from a sensor, and/or whether the sensor is available when it is needed.
When sensor enabled applications are deployed in a tightly-coupled manner along with their sensors (such as for remote healthcare provisioning and structure/building monitoring), applications can be conditioned to identify missing or misbehaving sensor readings. This is due to the strong linkage between information sources (sensors), phenomena observed and the data/information processors involved in assessing the situations observed. For example, for a congestive heart failure monitoring application, the coordination of sensory readings for electrocardiogram (ECG), blood pressure and weight may be involved (A. Misra, M. Blount, A. Kementsietsidis, D. M. Sow, M. Wang, “Advances and Challenges for Scalable Provenance in Stream Processing Systems,” IPAW 2008; M. Wang, M. Blount, J. Davis, A. Misra, D. M. Sow, “A Time-and-Value Centric Provenance Model and Architecture for Medical Event Streams,” HealthNet 2007). In this example, an ECG sensor on a patient in a remote health monitoring application can only provide ECG observations. Thus, any quality issues concerning the situation relate to the particular sensor (e.g., missing measurements, delayed measurements, or inaccurate measurement due to sensor malfunction or mis-calibration). These sensory readings are directly linked to the application and, therefore, if any of these sensors fail or in general misbehave, this can be uniquely tracked and identified. In this case, with complete knowledge of the sensors reporting to a sensor enabled application, the QoI provided to that application can easily be assessed from the operational characteristics of the sensors involved. The operational characteristics of sensors may include their physical characteristics (e.g., locations) and logical characteristics (e.g., interconnection topologies), as well as the health of the sensors. In cases such as that described above where sensor enabled applications are deployed in a tightly-coupled manner along with their sensors, ratios of the form: the number measurements received or used (“M”) over the maximum number of measurements expected (“N”), or “M/N”, are often used to reasonably capture the QoI collected.
Along with the previously described well planned and tightly coupled deployment of sensor networks and sensor enabled applications, dynamic sensor networks (e.g., ad hoc and/or mobile) are also emerging and becoming more commonplace. These dynamic sensor networks are deployed to either complement existing tightly coupled sensor networks, or they may exist on their own to be used on demand, for example when tightly coupled sensor networks are not practical. Such is the case when considering sensor networks that are deployed to support dynamically formed multi-party teams, for example for emergency response operations and military coalition intelligence, surveillance, target acquisition, and reconnaissance (I-STAR) operations; vehicle mounted sensors roaming city streets monitoring air quality and reporting traffic conditions, etc.; and social network inspired participatory sensing using body mounted sensory devices (e.g., smartphones), etc. Such deployments result in loosely coupled systems where applications and sensors are deployed separately and then bind and exchange information on demand and for transient periods of time.